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GitHub GitHub-Copilot Exam Overview:

Certification Vendor:GitHub, Microsoft
Exam Name:GitHub Copilot Certification Exam
Exam Number:GH-300
Available Languages:Portuguese (Brazil), English, Spanish, Korean, Japanese
Exam Duration:100 minutes
Certificate Validity Period:24 months
Real Exam Qty:60–65
Passing Score:700 / 1000 (approx. 70%)
Exam Format:Case study, Multiple-choice, Scenario-based, Multi-select
Exam Price:$99 USD
Recommended Training:GitHub Copilot Learning Path
GitHub Official Documentation
Exam Registration:Pearson VUE Registration
Official GitHub Certification Page
Sample Questions:GitHub GitHub-Copilot Sample Questions
Exam Way:Online proctored or in-person at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended: foundational GitHub knowledge, experience with at least one programming language, hands-on use of Copilot
Official Syllabus URL:https://learn.github.com/certification/COPILOT

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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
Topic 1
  • Responsible AI: This section of the exam measures the skills of AI Ethics Analysts and AI Developers and covers the principles of responsible AI usage, the risks associated with AI, and the limitations of generative AI tools. It includes the importance of validating AI-generated outputs and operating AI systems responsibly. It also explores potential harms such as bias, privacy concerns, and fairness issues, along with methods to mitigate these risks. The ethical considerations of AI development and deployment are also discussed.
Topic 2
  • Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.
Topic 3
  • Developer Use Cases for AI: This section of the exam measures skills of Full-Stack Developers and Cloud Engineers and covers how AI enhances developer productivity across various tasks such as learning new programming languages, debugging, writing documentation, and refactoring code. It discusses how GitHub Copilot integrates with the Software Development Lifecycle (SDLC) and its role in modernizing legacy applications. It also highlights the use of AI for personalized responses, sample data generation, and improving overall efficiency in software development.
Topic 4
  • Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.

GitHub CopilotCertification Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A company is currently storing code in Bitbucket and would like to use GitHub Copilot. Which GitHub Copilot plan will be most cost effective to allow them to manage users with their Identity Provider (e.g. Okta)?

Answer: C

Explanation:
For organizations that do not have a GitHub Enterprise Cloud (GHEC) subscription, the GitHub Copilot Business plan is indeed the most cost-effective option that supports an Identity Provider (IdP) like Okta.
Why this plan fits:
Cost: At $19 USD per user/month, it is significantly cheaper than the Copilot Enterprise plan ($39 USD per user/month).
IdP Support: It provides SAML SSO support, allowing you to manage user access through Okta even if your code is hosted elsewhere (e.g., Bitbucket).
No GHEC Requirement: Unlike the "Enterprise" tier, this specific SKU allows you to provide enterprise-style identity and access management without a full GitHub Enterprise setup.
Reference:
https://dev.to/codewithsimon/what-is-github-copilot-and-how-can-it-boost-your-productivity-14gg


NEW QUESTION # 23
Are there any limitations to consider when using GitHub Copilot for code refactoring?

Answer: B

Explanation:
GitHub Copilot's refactoring suggestions are generated from patterns in its training data and may not always follow the most optimized or best‑practice implementations. Developers should always review and refine any refactored code it produces to ensure correctness and quality.


NEW QUESTION # 24
When using GitHub Copilot to identify missing tests in your codebase, which of the following is the most important factor to consider?

Answer: D

Explanation:
GitHub Copilot needs the relevant code and test files in its context window to spot where tests are missing - without that context, it can't accurately identify gaps.


NEW QUESTION # 25
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

Answer: C

Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.


NEW QUESTION # 26
In what ways can GitHub Copilot contribute to the design phase of the Software Development Life Cycle (SDLC)?

Answer: A

Explanation:
During the design phase, Copilot can recommend relevant design patterns and industry best practices based on your code context and comments, helping you make more informed architectural decisions.


NEW QUESTION # 27
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